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Found 4 papers, 2 papers with code
Robust Learning from Untrusted Sources
Modern machine learning methods often require more data for training than a single expert can provide.
MACRO: A Meta-Algorithm for Conditional Risk Minimization
We study conditional risk minimization (CRM), i. e. the problem of learning a hypothesis of minimal risk for prediction at the next step of sequentially arriving dependent data.
Learning Intelligent Dialogs for Bounding Box Annotation
We demonstrate that (1) our agents are able to learn efficient annotation strategies in several scenarios, automatically adapting to the image difficulty, the desired quality of the boxes, and the detector strength; (2) in all scenarios the resulting annotation dialogs speed up annotation compared to manual box drawing alone and box verification alone, while also outperforming any fixed combination of verification and drawing in most scenarios; (3) in a realistic scenario where the detector is iteratively re-trained, our agents evolve a series of strategies that reflect the shifting trade-off between verification and drawing as the detector grows stronger.
Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (\"OAGM) will be held at IST Austria, on May 22-23, 2014.
